Mirjana Ivanović

TL;DR: This paper explores a new aspect of the dimensionality curse, referred to as hubness, that affects the distribution of k-occurrences: the number of times a point appears among the k nearest neighbors of other points in a data set, which becomes considerably skewed as dimensionality increases.

TL;DR: A recommendation module of a programming tutoring system - Protus, which can automatically adapt to the interests and knowledge levels of learners, is described, which shows suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.

TL;DR: Evidence is provided supporting the opinion that such a view is too simple, by demonstrating that distance-based methods can produce more contrasting outlier scores in high-dimensional settings, and novel insight is offered into the usefulness of reverse neighbor counts in unsupervised outlier detection.

TL;DR: An overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems is presented and various limitations of the current generation of social tagged systems and possible extensions that can provide better recommendation capabilities are considered.

TL;DR: This survey, an extension of the previous work, reports most relevant contemporary contributions in the field, aiming at assessing suitability of the ABC paradigm for the (current and future) IoT development.